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Epidermal Growth Factor Receptor Inhibition in the Management of Squamous Cell Carcinoma of the Lung

2016· review· en· W2234005477 on OpenAlexaff
Glenwood Goss, Johanna N. Spaans

Bibliographic record

VenueThe Oncologist · 2016
Typereview
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsOttawa HospitalOttawa Regional Cancer FoundationUniversity of Ottawa
Fundersnot available
KeywordsErlotinibMedicineOncologyErlotinib HydrochlorideEpidermal growth factor receptorInternal medicineLung cancerChemotherapyEGFR inhibitorsGefitinibCancer

Abstract

fetched live from OpenAlex

UNLABELLED: Molecular therapies targeting epidermal growth factor receptor (EGFR) have had a profound impact on the management of advanced non-small cell lung cancer (NSCLC). EGFR inhibition with EGFR tyrosine kinase inhibitors (EGFR-TKIs) and anti-EGFR monoclonal antibodies (mAbs) in squamous NSCLC (sqNSCLC) remains controversial in patients whose tumors are not known to harbor EGFR mutations. Recent meta-analyses of EGFR-inhibition randomized trials that are adequately powered for histological subgroup analysis and anti-EGFR trials limited to patients with squamous histology afford the opportunity to revisit EGFR treatment in sqNSCLC. In unselected patients with sqNSCLC who are not eligible for chemotherapy, EGFR-TKI therapy is a valid treatment option over placebo or best supportive care, with improved progression-free survival noted in randomized controlled trials in both the first- and second-line setting and improved overall survival (OS) in the second-line setting. In patients eligible for chemotherapy, first-line combination regimens with anti-EGFR mAbs have been shown to improve OS over chemotherapy alone in patients with squamous histology in meta-analysis and more recently in the SQUIRE sqNSCLC trial (chemotherapy with and without necitumumab). In sqNSCLC patients who respond to induction chemotherapy, maintenance therapy with erlotinib delays disease progression and may improve the survival of patients with stable disease. In the second-line setting, survival outcomes are comparable between chemotherapy and EGFR-TKIs in meta-analysis, with the latter being more tolerable as a second-line therapy. Newer-generation EGFR-TKI therapies may further benefit patients with sqNSCLC who have failed first-line chemotherapy, given the positive trial results from LUX-Lung 8 (afatinib vs. erlotinib). EGFR is a valid therapeutic target in unselected/EGFR wild-type patients with squamous cell carcinoma of the lung. With the recent approval of immune checkpoint inhibitors in the second-line management of advanced sqNSCLC and their adoption as a new standard of care, there exists an opportunity for novel combination therapies to increase therapeutic efficacy and durable tumor control. As more targeted agents are approved, combination regimens that include an anti-EGFR agent should be evaluated, and the optimal sequencing of targeted therapies should be defined. IMPLICATIONS FOR PRACTICE: Anti-epidermal growth factor receptor (EGFR) therapies remain controversial in unselected/wild-type EGFR squamous non-small cell lung cancer (NSCLC). Recent meta-analyses and squamous-only NSCLC EGFR-inhibition trials have overcome the power limitations of early trials and can now inform the management of squamous NSCLC with anti-EGFR therapies. With the approval of immunotherapeutics in the second-line management of squamous NSCLC, there exists an opportunity for novel combination therapies to improve efficacy and durable tumor control. The optimal timing and sequencing of available second-line targeted therapies, however, have yet to be defined. This review analyzes randomized clinical trials of EGFR inhibition in NSCLC and meta-analyses of these trials, with a focus on patients with squamous histology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.924
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.363
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations26
Published2016
Admission routes1
Has abstractyes

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